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Setting up Python Flask server on internet via Port forwarding

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In this post we will cover topics; Starting a server using Python Flask. Accessing server in Local Network. Adding a Port Forwarding rule in NAT settings. Accessing server in Internet via public IP. Difference between static IP and dynamic IP. Step 1 :- As the first step, we need to setup a server on our system.A server exposes your code to a particular port.Some examples are Apache Tomcat, Spring boot (inbuilt), Python Django (inbuilt), Nginx. Here we have a simple server in Python Flask. For a GET request in root path the server returns a "Hello World!".That's it. Going to start the server on my local IP address(192.168.1.105) at default flask port(5000) using below commands, $export FLASK_APP=flaskTest.py $flask run  --host=192.168.1.105 Step 2 :- For security purpose all ports for external communication is closed by router's firewall by default. So no communication can be done to outside world. For same reason our server won't be
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Automate Blog Post creation using Blogger APIs and Python

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This article will discuss the following topics, Blogger's REST APIs. Google's OAuth 2.0 playground. Accessing spreadsheet in Python. Creating blogger posts via Python. An experiment to automate blog post creation when you have structured data. I was searching for IFSC codes for some bank transactions and came across Reserve Bank's website, which has a list of Excel files containing Bank Codes, Contact Info and related details of each bank's branch. This time I will be looking towards Bharat co-operative bank of Mumbai. Here is how the corresponding data looks like. In the above picture, each row represents lots of details about each branch of the Bank. Let's think of creating a list of blog posts where each post gives IFSC codes of a particular branch along with address and contact details. As the first step, I have created a blog in blogger. In the Blogger dashboard, please also note the parameter called blog ID which is a kind of unique identifier
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Supervised Learning

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In supervised learning, we will be having the set of sample inputs and outputs. Our job is to create an algorithm with given data based on different parameters (that influence the generation of outputs). Supervised learning problems can be classified again into Regression and Classification problems. Regression Problems In the problems which fall under this category, the algorithm will be predicting an approximate value for the output. For example, consider a problem where we need to predict share price of stock in future based on its past records. The possible parameters could be - company's import, export data, changes in consumer markets etc. Classification Problems In this category, based on different parameters we will classify data to discrete valued outputs. So there won't be a prediction of output values, Instead will map to an output group. For example, consider a simple spam filter which categories e-mail as spam or not based on the presence of few
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The beginning of Data Quest

***** Work In Progress ******* Ever since I entered to IT industry, I was hearing about data science machine learning and the miracles happened because of these. On curiosity, I too have started learning some the concepts on these areas. As the first step, I have enrolled in Andrew N G's Machine Learning course. I will be sharing things I have learned in this blog and consider this post as an Index for my learning. Good Luck!!! Definition of Machine Learning  Supervised Learning Linear Regression Cost Function Gradient Descent Unsupervised Learning
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Setting up Jupyter Lab integrated with Python, Julia, R on Windows Subsystem for Linux (Ubuntu)

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Every guy gotta favourite OS. But, due to some constraints, all can't use that all the time. Being a Linux user, I had to shift to Windows 10 because of the barrier by MS Office and Dual booting often doesn't work. A few weeks before I learned about Windows Subsystem for Linux and my experience was great. If you are Linux pro stuck in Windows, this could be a relief. Basically, you can run a well-designed minimal version of Ubuntu inside Windows 10. In this article, we will get familiar with, Activation of Windows Subsystem for Linux. Installation of Ubuntu (no GUI) using Microsoft App Store. Configuring Python3, Pip3 and Jupyter Lab. Installation of R in Ubuntu and integration of R with Jupyter Lab. Installation of Julia in Ubuntu along with Jupyter Lab integration. Accessing Windows files with Ubuntu. Installation of Windows Subsystem for Linux Windows don't ship with WSL by default, we need to install it via PowerShell.  Reference -  https://docs.microsoft.co
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